Daily Excercise 21

Ecosystem Science and Sustainability 330

Download streamflow data from USGS

# load in necessary packages
library(dataRetrieval)

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library(tidymodels)

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library(dplyr)
library(tidyverse)

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library(tsibble)

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# Example: Cache la Poudre River at Mouth (USGS site 06752260)
poudre_flow <- readNWISdv(siteNumber = "06752260",    # Download data from USGS for site 06752260
                          parameterCd = "00060",      # Parameter code 00060 = discharge in cfs)
                          startDate = "2013-01-01",   # Set the start date
                          endDate = "2023-12-31") |>  # Set the end date
  renameNWISColumns() |>                              # Rename columns to standard names (e.g., "Flow", "Date")
  mutate(Date = yearmonth(Date)) |>                   # Convert daily Date values into a year-month format (e.g., "2023 Jan")
  group_by(Date) |>                                   # Group the data by the new monthly Date
  summarise(Flow = mean(Flow))                       # Calculate the average daily flow for each month

GET:https://waterservices.usgs.gov/nwis/dv/?site=06752260&format=waterml%2C1.1&ParameterCd=00060&StatCd=00003&startDT=2013-01-01&endDT=2023-12-31

1. Convert to tsibble

# Convert to tsibble (monthly data, with Date as index)
poudre_ts <- poudre_flow |> 
  as_tsibble(index = Date)

2. Plotting the time series

library(ggplot2)
library(plotly)

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# Basic ggplot of flow over time
p <- ggplot(poudre_ts, aes(x = Date, y = Flow)) +
  geom_line(color = "navyblue") +
  labs(title = "Monthly Average Flow of Cache la Poudre River",
       y = "Flow (cfs)", x = "Date") +
  theme_minimal()

# Animate with plotly
ggplotly(p)

3. Subseries

library(feasts)

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Loading required package: fabletools

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# use gg_subseries to visualize the data
subseries_plot <- poudre_ts %>%
  gg_subseries(Flow) +
  labs(title = "Monthly Subseries of Poudre River Flow") +
  theme_minimal()

print(subseries_plot)

4. Decompose

library(fabletools)

# STL decomposition with a seasonal window (say 13 months for annual seasonality)
decomp <- poudre_ts |> 
  model(STL(Flow ~ season(window = "periodic"))) |> 
  components()

# Plot the components
autoplot(decomp)